Deep Learning Models Performance Evaluations for Remote Sensed Image Classification

Deep learning-based land cover and land use (LCLU) classification systems are a significant aspiration for remote sensing communities. In nature, remote sensing images have various properties that need to be analyzed. Analyzing and interpreting image properties is difficult due to the nature of the image, the sensor technology's capability, and other determinant variables such as seasons and weather conditions. The problem is essential for environmental monitoring, agricultural decision-making, and urban planning if it can be supported by deep learning systems. Therefore, deep learning approaches are proposed to quickly analyze and interpret the remote sensing image to classify the LCLU. The deep learning methods could be designed starting from scratch or using pre-trained networks. However, there are few comparisons of deep learning methods developed from scratch and trained on pre-trained networks. Thus, we proposed evaluating and comparing the deep learning models convolutional neural network feature extractor (CNN-FE) by developing it from scratch, transfer learning, and fine-tuning it for the LCLU classification system using remote sensed images. Using CNN-FE, TL, and fine-tuning deep learning models as examples, this paper compares and analyzes deep learning algorithms for remote sensed image classification. After developing and training each deep learning model on the UCM dataset, we evaluated and compared their performances using the performance measurement metrics accuracy, precision, recall, f1-score, and confusion matrix. The proposed deep learning algorithms can adapt and learn the features of the remote sensing images, and the TL and fine-tuning classification performances are significantly improved. As a result of the efficient time used for training the models, this paper discovered that the fine-tuned deep learning model achieved profound accuracy performance results in the UCM dataset.